Recognizing the Type of Human Activities using Inferred Deep Learning Networks and and Intuitive Fuzzy Sets

Abstract

Human Activity Recognition refers to the identification and analysis of the activities carried out by an individual or a group of individuals. This research introduces a highly effective system for recognizing human activities. The system utilizes data from wearable sensors and employs advanced deep learning algorithms. The proposed system utilizes a fusion of convolutional neural network and Long Short Term Memory (LSTM) neural network to extract the sophisticated characteristics of the sensor data and to acquire knowledge of the patterns based on the temporal sequence of the data. Additionally, this article employs a Soft-max based classifier for the final connected layer of the neural network. This classifier utilizes fuzzy classification to assign the output of LSTM blocks to relevant activity classes. We chose to utilize this classifier due to the fact that sensor data associated with human-like movements, such as walking and running, or opening and closing a door, typically exhibit significant similarities. Enhancing the Soft-max classifier with fuzzy inference capabilities improves its accuracy in distinguishing between closely related activities. In addition, this article introduces a post-processing module that takes into account the classification of actions over an extended time period. Through the utilization of the proposed fuzzy Soft-max classifier and the subsequent post-processing approach, we attained a commendable accuracy rate of 97.03% on the PAMAP2 dataset.